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Two-sample testing

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.

Papers

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Showing 201–225 of 338 papers

TitleStatusHype
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations—0
Differentially Private False Discovery Rate Control—0
Diagonal Discriminant Analysis with Feature Selection for High Dimensional DataCode0
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data—0
Local Private Hypothesis Testing: Chi-Square Tests—0
Locally Private Hypothesis Testing—0
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference—0
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels—0
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing—0
Second-Order Asymptotically Optimal Statistical Classification—0
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets—0
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?—0
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching—0
Testing Identity of Multidimensional Histograms—0
Unsupervised Textual Grounding: Linking Words to Image Concepts—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Closing the AI Knowledge Gap—0
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis—0
Generalized Binary Search For Split-Neighborly Problems—0
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit—0
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting—0
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review—0
PacGAN: The power of two samples in generative adversarial networksCode0
Adaptive Active Hypothesis Testing under Limited Information—0
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All datasetsBlob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data SetMNIST vs Fake MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy98.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy74.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy65.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy57.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy91—Unverified